[SciPy-User] how to use properly the function fmin () to scipy.optimize
javi
fralosal@ei.upv...
Tue Mar 13 08:02:11 CDT 2012
Hello, I have been trying to find the right way to use the function fmin () to
use downhill simplex.
Mainly I have a problem with that is that the algorithm converges to good
effect, ie as a solution with a value next to zero.
To test the performance of the algorithm I used the following example:
def minimize (x):
min = x [0] + x [1] + x [2] + x [3]
return min
In which given a vector x would want to obtain the values of its elements that
when added give the minimum possible value.
To do this use the following function call:
solution = fmin (minimize, x0 = array ([1, 2, 3, 4]), args = "1", xtol = 0.21, =
0.21 ftol, full_output = 1)
print "value parameters", solution [0], "\ n"
and I get the following results:
Optimization terminated successfully.
Current function value: 10.000000
Iterations: 1
Function evaluations: 5
value of the parameters: [1. 2. 3. 4.]
As you can see the solution is VERY BAD, and I understand that due to large
values of ftol and xtol that I gave it converges very quickly and gives a
small value.
Now, for that is a better result, ie, better than the 10 found understand that I
must decrease and ftol xtol values, but in doing so I get:
"Warning: Maximum number of function evaluations exceeded Has Been."
Where I understand the algorithm before converging has made excessive calls to
the function "minimize".
Could you tell me what the correct use of the parameters ftol and xtol to find
a good minimum next to 0?. Sshould generally be used in subsequent cases of ftol
and xtol values?, They differ?.
A greeting and thank you very much.
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